Given the comment \(C_{ij}\)  to be classified, CASCADE leverages content and context-based information from the comment. For content-based modeling of \(C_{ij}\) , a CNN is used to generate the representation vector \(\vec{\mathbf {c}}_{i,j}\)  for a comment. CNNs generate abstract representations of text by extracting location-invariant local patterns. This vector \(\vec{\mathbf {c}}_{i,j}\)  captures both syntactic and semantic information useful for the task at hand. For contextual modeling, CASCADE first learns user embeddings and discourse features of all users and discussion forums, respectively (Section REF ). Following this phase, CASCADE then retrieves the learnt user embedding \(\vec{\mathbf {u}}_{i}\)  of user \(u_i\)  and discourse feature vector \(\vec{\mathbf {t}}_{j}\)  of forum \(t_j\) . Finally, all three vectors \(\vec{\mathbf {c}}_{i,j}\) , \(\vec{\mathbf {u}}_{i}\) , and \(\vec{\mathbf {t}}_{j}\)  are concatenated and used for the classification (Section REF ). One might argue that instead of using one CNN, we could use multiple CNN (explained in [1]}) to get better text representations whenever a comment contains multiple sentences. However that is out of the scope of this work. Here, we aim to show the effectiveness of user specific analysis and context-based features extracted from the discourse. Also the use of a single CNN for text representation helps to consistently compare with the state of the art.
